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Physical programming and conjoint analysis-based redundancy allocation in multistate systems: A Taguchi embedded algorithm selection and control (TAS&C) approach

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  • V V Kumar
  • M Tripathi
  • M K Pandey
  • M K Tiwari

Abstract

Amidst increasing system complexity and technological advancements, the manufacturer aims to win the consumer's trust to maintain his or her permanent goodwill. This expectation directs the manufacturer to address the problem of attaining desired quality and reliability standards; hence, the measure of performance of a system in terms of reliability and utility optimization poses an issue of primary concern. In order to meet the requirement of a reliable and trouble-free product, optimal allocation of all conflicting parameters is essential during the design phase of a system. With this in mind, this paper presents a physical programming and conjoint analysis-based redundancy allocation model (PPCA-RAM) for a multistate series—parallel system. Use of physical programming approach is the key feature of the proposed algorithm to eliminate the need for multi-objective optimization. Physical programming methodology provides an adequate balance among various associated performance measures and thus provides an efficient tool for formulating the objective function of a practical redundancy allocation problem. The proposed model has been addressed by a novel methodology called Taguchi embedded algorithm selection and control (TAS&C). An illustrative example has been presented to authenticate the efficiency of the proposed model and algorithm. The results obtained are compared with the genetic algorithm (GA), artificial immune system (AIS), and particle swarm optimization (PSO), where TAS&C was seen to significantly outperform the rest.

Suggested Citation

  • V V Kumar & M Tripathi & M K Pandey & M K Tiwari, 2009. "Physical programming and conjoint analysis-based redundancy allocation in multistate systems: A Taguchi embedded algorithm selection and control (TAS&C) approach," Journal of Risk and Reliability, , vol. 223(3), pages 215-232, September.
    • Handle: RePEc:sae:risrel:v:223:y:2009:i:3:p:215-232
    DOI: 10.1243/1748006XJRR210
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    References listed on IDEAS

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    1. Limbourg, Philipp & Kochs, Hans-Dieter, 2008. "Multi-objective optimization of generalized reliability design problems using feature models—A concept for early design stages," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 815-828.
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